What is mRMR?
A. It means minimum-Redundancy-Maximum-Relevance feature/variable/attribute selection. The goal is to select a feature subset set that best characterizes the statistical property of a target classification variable, subject to the constraint that these features are mutually as dissimilar to each other as possible, but marginally as similar to the classification variable as possible. We showed several different forms of mRMR, where “relevance” and “redundancy” were defined using mutual information, correlation, t-test/F-test, distances, etc. Importantly, for mutual information, we showed that the method to detect mRMR features also searches for a feature set of which features jointly have the maximal statistical “dependency” on the classification variable. This “dependency” term is defined using a new form of the high-dimensional mutual information. The mRMR method was first developed as a fast and powerful feature “filter”. We then also showed a method to combine mRMR and “wrapper” sel
